1. Guide to Using Excel For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 5th Ed. Chapter 15: Analyzing and Forecasting Time Series Data By Groebner, Shannon, Fry, & Smith Prentice-Hall Publishing Company Copyright, 2005

2. Chapter 15 Excel Examples  Trend Based Forecasting Trend Based Forecasting Taft Ice Cream Company  Nonlinear Trend Nonlinear Trend Harrison Equipment Company  Seasonal Adjustment Seasonal Adjustment Big Mountain Ski Resort  Single Exponential Smoothing Single Exponential Smoothing Humboldt Electronics Company More Examples

3. Chapter 15 Excel Examples  Double Exponential Smoothing Double Exponential Smoothing Billingsley Insurance Company

4. Trend Based Forecasting - Taft Ice Cream Company Issue: The owners of Taft Ice Cream Company considering expanding their manufacturing facilities. The bank requires a forecast of future sales. Objective: Use Excel to build a forecasting model based on 10 years of data. Data file is Taft.xls

5. Open File Taft.xls Trend Based Forecasting – Taft Ice Cream Company

6. First click on Chart Wizard, then select Line Chart Trend Based Forecasting – Taft Ice Cream Company

7. Click on Series tab and define range for Data Variables. Click on Next. Trend Based Forecasting – Taft Ice Cream Company

8. Remove unneeded data sets and identify the range for the X Variable. Trend Based Forecasting – Taft Ice Cream Company

9. Label the axes and graph Trend Based Forecasting – Taft Ice Cream Company

10. Size and format the graph as needed. Trend Based Forecasting – Taft Ice Cream Company

11. To develop the linear model, start with the original data. Trend Based Forecasting – Taft Ice Cream Company

12. Click on Tools, then Data Analysis and finally Regression Trend Based Forecasting – Taft Ice Cream Company

13. Define the data range for the X and Y Variables. Use the t column for time. Trend Based Forecasting – Taft Ice Cream Company

14. The regression output determines the slope and intercept of the linear model. Trend Based Forecasting – Taft Ice Cream Company

15. To visually compare the data with a linear model, return to the graph constructed using the chart wizard. Trend Based Forecasting – Taft Ice Cream Company

16. Left click on any data point, then right click and select Add Trendline Trend Based Forecasting – Taft Ice Cream Company

17. Select Linear model then click on Options Tab. Trend Based Forecasting – Taft Ice Cream Company

18. Select Display equation on chart. Trend Based Forecasting – Taft Ice Cream Company

19. Format chart as desired. Trend Based Forecasting – Taft Ice Cream Company

20. To determine both MAD and MSE values, start by selecting the Residuals option in Regression analysis. Trend Based Forecasting – Taft Ice Cream Company

21. The Predicted values and Residuals become part of the regression output. Trend Based Forecasting – Taft Ice Cream Company

22. Write and copy a formula to determine the Squared Residual values. Trend Based Forecasting – Taft Ice Cream Company

23. Also write and copy a formula to find the absolute values of the residuals. Trend Based Forecasting – Taft Ice Cream Company

24. Sum the squared and absolute values of the residuals. Trend Based Forecasting – Taft Ice Cream Company

25. Divide both summed values to find the MSE and MAD values. Trend Based Forecasting – Taft Ice Cream Company

26. Issue: Harrison Equipment is interested in forecasting future repair costs for a crawler tractor it leases to contractors. Harrison Equipment is interested in forecasting future repair costs for a crawler tractor it leases to contractors.Objective: Use Excel to develop a nonlinear forecasting model. Data file is Harrison.xls Nonlinear Trend - Harrison Equipment Company Nonlinear Trend - Harrison Equipment Company

27. Open File Harrison.xls This tutorial will start by finding the trend line. It will also show how to find residuals. Nonlinear Trend – Harrison Equipment Company

28. First click on Chart Wizard, then select Line Chart Nonlinear Trend – Harrison Equipment Company

29. Click on Series tab and define range for both Y and X Variables. Nonlinear Trend – Harrison Equipment Company

30. Format, size and label chart as desired. Nonlinear Trend – Harrison Equipment Company

31. To add trendline, left click on any data point, then right click and select Add Trendline Nonlinear Trend – Harrison Equipment Company

32. Choose Linear Nonlinear Trend – Harrison Equipment Company

33. Trendline appears. Nonlinear Trend – Harrison Equipment Company

34. To build linear model, click on Tools, then Data Analysis and finally Regression Nonlinear Trend – Harrison Equipment Company

35. Define the data range for the X and Y Variables. Use the t column for time. Also ask for residuals. Nonlinear Trend – Harrison Equipment Company

36. The regression output determines the slope and intercept of the linear model. Nonlinear Trend – Harrison Equipment Company

37. Calculate bye MAD value by first finding the absolute value of the residuals using the ABS function. Nonlinear Trend – Harrison Equipment Company

38. Sum the absolute value of the Residuals and divide by the count (number) of residuals to find the MAD. Nonlinear Trend – Harrison Equipment Company

39. To develop nonLinear model define a new variable found by squaring the time values. Nonlinear Trend – Harrison Equipment Company

40. Develop a new regression model with t 2 value as the independent variables. The model becomes y = a + bt 2. The output gives the new regression coefficients. Nonlinear Trend – Harrison Equipment Company

41. Using the same SUM and COUNT formula find the MAD for the nonlinear model. Nonlinear Trend – Harrison Equipment Company

42. To plot the nonlinear model, define a new column of values determined by plugging the values of t 2 into the regression model. Nonlinear Trend – Harrison Equipment Company

43. Use the Chart Wizard, Line options to develop a graph comparing the observed values with the nonlinear model. You will identify two Series. Nonlinear Trend – Harrison Equipment Company

44. Format and place chart as needed. Nonlinear Trend – Harrison Equipment Company

45. Seasonal Adjustment - Big Mountain Ski Resort Seasonal Adjustment - Big Mountain Ski Resort Issue: The resort wants to build a forecasting model from data that has a definite seasonal component. Objective: Use Excel to develop a forecasting model adjusting for seasonal data. Data file is Big Mountain.xls

46. Open File Big Mountain.xls Seasonal Adjustment – Big Mountain Ski Resort

47. To develop the graph first click on Chart Wizard button then select Line. Seasonal Adjustment – Big Mountain Ski Resort

48. Define the range for the Y and X variable values. Seasonal Adjustment – Big Mountain Ski Resort

49. Size and format the graph as desired. Seasonal Adjustment – Big Mountain Ski Resort

50. To find the moving average values use the AVERAGE function. Seasonal Adjustment – Big Mountain Ski Resort

51. Use the AVERAGE function again to find the centered moving average. Seasonal Adjustment – Big Mountain Ski Resort

52. Write a simple formula to find the Ratio to Moving Average values. Seasonal Adjustment – Big Mountain Ski Resort

53. To find the season index values click on PHStat, then Data Preparation and then Unstack. Seasonal Adjustment – Big Mountain Ski Resort

54. To find the seasonal index values start by adding (SUM) the three ratio to moving average values for each season. Seasonal Adjustment – Big Mountain Ski Resort

55. Divide the total values to find the seasonal index numbers. Seasonal Adjustment – Big Mountain Ski Resort

56. Write a formula to select the correct seasonal value to use to deseasonalize the data. Copy the formula into all cells. Seasonal Adjustment – Big Mountain Ski Resort

57. Use the Select Chart Wizard to graph the deseasonalized data. Seasonal Adjustment – Big Mountain Ski Resort

58. Format and place the chart as desired. Seasonal Adjustment – Big Mountain Ski Resort

59. Use the Tools, Data Analysis, Regression option to develop a regression model of the deseasonalized data. Seasonal Adjustment – Big Mountain Ski Resort

60. Single Exponential Smoothing Humboldt Electronics Issue: The company needs to develop a forecasting model to help make inventory decisions, and wants the model to give more weight to recent values than to regression model do. Objective: Use Excel to develop a single exponential smoothing forecasting model. Data file is Humboldt.xls

61. Open File Humboldt.xls Single Exponential Smoothing – Humboldt Electronics

62. Click on the Chart Wizard button then select Line. Single Exponential Smoothing – Humboldt Electronics

63. Click on the Series tab, then identify ranges for the Y and X variables. Single Exponential Smoothing – Humboldt Electronics

64. Label the axes. Single Exponential Smoothing – Humboldt Electronics

65. Format and place graph as desired. Single Exponential Smoothing – Humboldt Electronics

66. To develop the exponential smoothing model, return to the original data. Set initial forecast to 400 and write formula for following forecasts. Single Exponential Smoothing – Humboldt Electronics

67. Write similar formula to determine Forecast for Period 11. Single Exponential Smoothing – Humboldt Electronics

68. To determine MAD start by writing formula to find the forecast error. Single Exponential Smoothing – Humboldt Electronics

69. Find absolute value of forecast error. Then write a formula to find MAD. Single Exponential Smoothing – Humboldt Electronics

70. Construct a graph of actual and forecast sales by returning to Chart Wizard, Line option. Identify the two Series to graph. Single Exponential Smoothing – Humboldt Electronics

71. Format and place graph as desired. Single Exponential Smoothing – Humboldt Electronics

72. Issue: The claims manager has data for 12 months and wants to forecast claims for month 13. But the time series contains a strong upward trend Objective: Use Excel to develop a double exponential smoothing model. Data file is Billingsley.xls Use Excel to develop a double exponential smoothing model. Data file is Billingsley.xls Double Exponential Smoothing Billingsley Insurance

73. Open file Billingsley.xls Double Exponential Smoothing – Billingsley Insurance

74. Click on the Chart Wizard button then select Line. Double Exponential Smoothing – Billingsley Insurance

75. Click on the Series tab, then identify ranges for the Y and X variables. Double Exponential Smoothing – Billingsley Insurance

76. Label the axes. Double Exponential Smoothing – Billingsley Insurance

77. Format the graph as desired. Double Exponential Smoothing – Billingsley Insurance

78. To develop the double exponential smoothing equations, return to the data sheet and determine the initial values for C and T. Double Exponential Smoothing – Billingsley Insurance

79. Use Equations 13-18 and 13-19 to determine the values for the Constant and Trend. Double Exponential Smoothing – Billingsley Insurance

80. Use Equation 13-20 to determine the Forecast values and the Forecast for Period 13. Double Exponential Smoothing – Billingsley Insurance

81. Write equations to find both the Forecast Error and Absolute Forecast Error. Double Exponential Smoothing – Billingsley Insurance

82. Write equations to find both the Total Absolute Error and the MAD. Double Exponential Smoothing – Billingsley Insurance